Master Data Management can be very complex and organization specific. A properly detailed and organized RFP can help avoid:
Make the RIGHT MDM system selection!
Compared to other enterprise-wide software systems, Master Data Management (MDM) presents very unique and complex issues that must be addressed when trying to evaluate and select the MDM system best for your organization.
Before doing anything else, a comprehensive master data management (MDM) strategy must be developed. While it is beyond the scope of this document, developing an MDM strategy can be best summarized by stating it requires close examination of your organizational structure, it's current data governance policies, reporting systems, integration of workflow processes, security needs, and any other issue that affects master data quality.
Once an MDM strategy has been developed, the master data management software needed to enable that strategy must be selected and implemented, a task that is easier said than done. At Infotivity we have heard many disaster stories told by customers who tried to implement an MDM system and failed. Some found their MDM to be limited in scope, i.e., to just one entity, or just one business unit. Many found they had worked very hard to simply trade "data silos" for broader, more complicated "master data management silos". A silo is a silo, as they say. The same problems with a different name.
Since an MDM system is so complex, many experts recommend using a detailed MDM Request for Proposal (RFP) to communicate requirements to potential vendors. Using a comprehensive RFP is definitely the best approach, assuming the RFP is correctly prepared, with sufficient detail and properly organized. But that's the catch - most RFPs are NOT correctly prepared. Many of the MDM problems we hear of started because of flaws or omission in the RFP requirements used, and over time we have been able to put together some trends and patterns, each of which is discussed below. Please note that these and others are addressed in our 3,810 industry standard MDM selection requirements criteria.
Many experts adhere to the concept - when doing MDM, you are really implementing data governance - and it could not be more true. But it also means that a data governance policy must be present before it can be implemented. It becomes even more complicated by the fact that data governance is unique to each company's culture, business processes, and IT environment. But many companies we have spoken with selected an MDM platform without much thought to their data governance needs at the departmental or enterprise level. (NOTE: Infotivity's MDM system RFP can be easily used as a detailed data governance checklist.)
It is extremely important the MDM platform you select is able to support the data governance policies and processes as defined by your company's needs specifically. If it can't, your data governance design could be compromised significantly by forcing it to fit into the limitations of some MDM system, since many have fixed or very rigid data models and functionality.
Audits and control functions are very important components of any data governance policy. To properly support these functions, especially if they are already in place and performing well, the RFP should verify that a vendor's MDM system integrates with a company's reporting and security tools to provide immediate and reliable access to data and data quality metrics.
The Infotivity MDM RFP Master dedicates an entire section to Data Governance, and queries vendors about many criteria relating to the issues outlined here. RFP questions typical of this section are:
One of the key goals that must be met when laying out your MDM is to ensure it is capable of managing multiple business data entities at the same time, all with the same software platform. Examples of these data entities could be customers, products and/or business organizations. This reduces and simplifies system maintenance, which results in a much lower total cost of ownership (TCO).
It is very difficult to identify the business groups and functionality that an enterprise MDM system may need to address in the future. The need to address multiple entities must be built into the MDM system from the beginning.
NOTE: The Infotivity RFP queries vendors about this in detail.
Some argue that an effective alternative is to deploy, then manage, a separate master data solutions for each different business data entity as needed. But that approach becomes very cumbersome and expensive (and even more complex!) because of the additional integration efforts and system maintenance needed on an on-going basis.
There are many reasons why it is important to raise the question of how well an MDM platform will integrate with the standard workflow tool your organization already using.
Workflow is an important to both MDM and data governance because it could be used during the creation and maintenance of a master data entity. Workflow functions are also used to automatically monitor the data as it is entered or acquired, and automatically alert the data steward (the employee who is responsible for the data) to any data quality issues as appropriate.
Several MDM vendors bundle their own workflow tool since it is so important, but bear in mind this may not offer integration with your own standard workflow tool. Why go through the expense of re-implementing your existing workflows in a new system?
The Infotivity RFP Master queries vendors about more than 125 criteria relating to workflow capabilities and integration. Some typical examples are:
A well-planned master data management system must treat the relationships and hierarchies that can and do exist between entities as part of the master data. This is very important because data is not consistent.
A good example would be a large chain of retail stores stocking only certain products in certain regions of the country, with individual store managers setting the price of that product in their own store. Another example, again in a retail chain setting, is purchasing locally produced items for sale in each region's stores, for multiple regions, with each store price individually set, all under the same SKU nationally. Last, for an outdoor equipment manufacturer's customer service, what about tracking service calls in multiple climates back to the component level, with the components being sourced by multiple manufacturers using different materials.
To be successful, an MDM system RFP must query vendors about how their solution models complex B2B and B2C relationships and hierarchies, along with how they are defined in those master data entities, all from within the same MDM platform.
The Infotivity RFP queries vendors about these issues in detail, and even provides an entire section dedicated to managing hierarchies. An RFP criteria typical of this section is:
Data cleansing operations such as name corrections, address standardizations, and data transformations should always be performed within the MDM platform itself, never outside of the MDM system at the source using commonly available data quality tools. (a lot of other RFPs ask about data cleaning, but typically those RFPs fail to ask how and where the data will be cleansed - a big omission!)
The reason for this fact is simple - the number of data sources in an enterprise MDM rollout can span multiple departments and could be coming from tens or even hundreds of systems. In this scenario cleansing the data at the source systems is simply not viable due to the high probability of mistakes and omissions. In a large enterprise wide system the data cleansing needs to be centralized within the MDM system to maintain control and data quality.
In the case your company has already implemented a cleansing tool, then it is important to be sure the planned MDM solution has the ability to integrate with the cleansing tool "out-of-the-box". The Infotivity RFP queries vendors about these issues in detail.
"Matching" simply refers to the techniques used by the MDM system to reconcile the data. The driving force behind any system is matching. But the trick missed by many is that matching is in eye of the beholder. Right now there are several types of matching techniques in use, such as probabilistic, deterministic, and empirical. No one technique has the ability to address all possible types of data errors and/or variations in the master data. The Infotivity RFP investigates these issues thoroughly.
In order to achieve the most reliable, constant, consolidated view of the master data, the MDM platform should simultaneously use a combination of these matching techniques, with each able to address a particular set of data matching issues.
A golden record is THE single version of the truth. The single standard. Without a golden record, you don't have MDM, since MDM needs a single point of truth in order to measure data in all processes across the enterprise. Many so-called MDM systems simply link identical data sources together in a register of some type, but do not resolve data inconsistencies.
To be truly beneficial, master data from different sources should first be reconciled and then centrally stored within a master data repository. In a large enterprise, the potential number of sources across the organization and the volume of master data can be quite high, and it is important that the MDM system is able to automatically create a true golden record for any master data type, whether it be for a customer, product, asset, or whatever.
In addition, a robust MDM system usually provides a comprehensive "unmerge" facility to roll back any manual errors or exceptions. In large organizations where several data stewards are involved with managing master data this type of activity is very time consuming. The Infotivity RFP queries vendors about these issues in detail.
Implementing a successful master data management system requires that so many issues be addressed in the data governance - extraction, transformation, cleansing, matching, and workflow arenas that it is very easy to forget another, very important issue called REGULATORY COMPLIANCE.
It is very important to maintain the history and lineage of all master data, otherwise there will be a huge price to pay, and a lot of hair pulling, during the next AUDIT or COMPLIANCE CHECK.
Today's business and regulatory climate demands clean, accurate data, but it also requires VALIDATION that the data is indeed reliable. This validation poses an extremely complex challenge, since the master data is being updated on a continuous basis from many different source systems in real time as transactions take place.
A history of all the changes made to the master data, showing the source lineage and how the data was changed, needs to be captured in the system in some fashion. This becomes the foundation for the clear audit trail critically important to data governance and regulatory compliance reporting. The Infotivity RFP investigates these issues thoroughly.
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